The Fondation Sciences Mathématiques de Paris (FSMP) provides funding for 20 years of post-doctoral fellowships in mathematics and in computer science for the 2018-2019 academic year. (Meaning you cannot expect a position in Paris for the coming 20 years!) Appointed fellows will hold one or two-year positions in affiliated research laboratories, starting in October 2018. This program is supported by Université Paris Sciences Lettres (PSL). If you are interested in applying for one of these fellowships, please register online. Note that the deadline is December 1!

## Archive for mathematics

## 20 postdoc positions in Paris!

Posted in Kids, pictures, University life with tags Fondation des Sciences Mathématiques de Paris, FSMP, mathematics, Paris, Paris Sciences et Lettres, postdoctoral position, PSL on November 22, 2017 by xi'an## Grothendieck’s papers on-line!

Posted in Books, Kids, University life with tags Alexandre Grothendieck, mathematics, notes, Université de Montpellier on May 10, 2017 by xi'an**T**oday, the University of Montpellier will put on-line the series of 18,000 pages of manuscripts of Alexandre Groethendieck that it had digitised a few years ago. Thanks to the efforts of Jean-Michel Marin, the nearly incomprehensible legal imbroglio on the rights of both the University and the children of Groethendieck has been unravelled, meaning that the University is now allowed to make the manuscripts available, while the children have the sole property of the dozens of thousands of pages written by Groethendieck till his death. It is hard to imagine how such a volume can be efficiently explored and exploited to uncover new mathematical advances made by Groethendieck in the last and secluded part of his life, but at last the raw material is available for all to try.

## Abel Prize goes to Yves Meyer

Posted in Books, pictures, University life with tags Abel Prisen, Abel Prize, École Normale de Cachan, Gauss Prize, mathematics, Norway, prizes, wavelets, Yves Meyer on March 21, 2017 by xi'an**J**ust heard the great news that the Abel Prize for 2017 goes to Yves Meyer! Yves Meyer is an emeritus professor at École Normale de Cachan and has produced fundamental contributions to number theory, operator theory and harmonic analysis. He is one of the originators of the theory of wavelets and multiresolution analysis. Among other recognitions and prizes, he was an invited speaker at the International Congress of Mathematicians in 1970 (Nice), in 1983 (Warsaw), and in 1990 (Kyoto), and was awarded the Gauß Prize in 2010. Congratulations and total respect to Yves Meyer!!!

## weapons of math destruction [book review]

Posted in Books, Kids, pictures, Statistics, University life with tags AIs, algorithms, artificial intelligence, Artificial Intelligence and Statistics, big data, data privacy, machine learning, mathematics, population prediction, quant, regression, Statistics, superintelligence, Wall Street, weapons of math destruction, zero hour contract on December 15, 2016 by xi'an **A**s I had read many comments and reviews about this book, including one by Arthur Charpentier, on Freakonometrics, I eventually decided to buy it from my Amazon Associate savings (!). With a strong a priori bias, I am afraid, gathered from reading some excerpts, comments, and the overall advertising about it. And also because the book reminded me of another quantic swan. Not to mention the title. After reading it, I am afraid I cannot tell my ascertainment has changed much.

“Models are opinions embedded in mathematics.” (p.21)

The core message of this book is that the use of algorithms and AI methods to evaluate and rank people is unsatisfactory and unfair. From predicting recidivism to fire high school teachers, from rejecting loan applications to enticing the most challenged categories to enlist for for-profit colleges. Which is indeed unsatisfactory and unfair. Just like using the h index and citation ranking for promotion or hiring. (The book mentions the controversial hiring of many adjunct faculty by KAU to boost its ranking.) But this conclusion is not enough of an argument to write a whole book. Or even to blame *mathematics* for the unfairness: as far as I can tell, mathematics has nothing to do with unfairness. Some analysts crunch numbers, produce a score, and then managers make poor decisions. The use of *mathematics* throughout the book is thus completely inappropriate, when the author means statistics, machine learning, data mining, predictive algorithms, neural networks, &tc. (OK, there is a small section on Operations Research on p.127, but I figure deep learning can bypass the maths.) Continue reading

## Warwick campus

Posted in pictures, Travel, University life with tags England, heron, mathematics, Statistics, summer, University of Warwick on September 3, 2014 by xi'an## {Monte Carlo}²

Posted in Kids with tags comics, logic, mathematics, Monte Carlo methods, SMBC on May 1, 2012 by xi'an## teachin’ (math?) stat…

Posted in Statistics, Travel, University life with tags France, mathematics, Statistics, teaching, undergraduates on January 24, 2012 by xi'an**A**rthur Charpentier (from the awesome Freakonometrics) pointed out to me those two blogs about teaching statistics. One by Meg Dillon about the joy of teaching statistics in France, of all places!, and entitled Statistics à la Mode. And another one by Douglas Andrews commenting on the first one, entitled the Big Mistake: teaching stat as though it was math… (It appeared on an ASA community blog/forum I did not know about.)

“

…there is almost invariably a peculiar pair of caveats presented as from on high: Never accept the alternative hypothesis, and ever say the probability is 0.95 that the mean lies in a 95% confidence interval for the mean.” Meg Dillon, After Math

**B**oth blogs managed to bemuse me *(this is not going to be a very coherent post, I am afraid!)*: the first one because it has this condescending tone of pure mathematicians about statistics or at least statistics course (i.e. “anyone can teach statistics!” mixed with “I hate teaching statistics!”) that I meet too often, esp. this side of the pond. Plus it seemed to miss the fundamental distinction between probability and statistics (check the above quote). And it did not say why the contents of the French course was much nicer than the equivalent designed by Meg Dillon at her university (except for the fact that she could use measure theory from the start). Maybe the French idiosyncrasy the author basks in is the fact that statistics is not recognised as a field in French universities (there is no stat department for instance) but is instead a subfield of mathematics…

“

…stat is a different intellectual discipline. She longs for a so-called stat course based on sigma-algebras and probability spaces. Well, that has been tried many times over many years, and it fails miserably at helping students understand the important stat concepts.” Douglas Andrews, ASA Blog Viewer

**T**he second post is making sense in stressing that stat is not math. (Or rather, as it should have been stated, it is not *only* math.) And that (non-statistician) mathematicians should get some preliminary training or exposure to real data when teaching statistics courses. I can certainly remember a few of my (French) stat teachers who had never approached data in their whole life! However, the comment that “foundation of stat is in empirical science and in learning from observed data, not in math” seems to go overboard. As it echoes in negative the complaint from the math teacher that intro statistics courses were “a hodgepodge of recipes” with no mathematical backbone. My feeling there is that, while we certainly do not need measure theory for the earliest statistics courses (Riemann integration is good enough for my second and third year students), we have to anchor statistical techniques into a mathematical bed to avoid them looking as a bag of tricks. I remember after my first (mathematical) statistics course on being puzzled by the lack of direction and/or the multiplicity, when compared with a standard math course. I was missing the decision-theoretic part that was to come later! Had I been exposed to a non-mathematical intro stat course, I do not think I would have persevered in this field! (And I would have moved to differential geometry instead…)